I’ve been sitting with the passing of Kobe Bryant for a few weeks now. I was a bit too young and removed to be aware of the rape allegations at the time. I wrote him off when I heard about it later.

I was never really a Bryant fan. My early memories were frustrated. I’d race home from school to find ESPN once again scheduled a Lakers beatdown of a terrible team. Rather than an actual game, between say the Pistons and Heat or Jazz. The Lakers, Bryant, were the only ones Australians wanted to watch, apparently.

Bryant had an incredible record. He was a champion, an MVP, and, until recently, third all time in NBA scoring. But I’d watch him throw up ridiculous shot after ridiculous shot. Think about all the talent on the bench with half the opportunities. It felt like we were all giving him a bit much. Or, rather, he was taking it.

The details of the sexual assault case in 2003 make clear that Kobe’s self-obsession often came at others’ expense. In this case, a nineteen-year-old girl. The criminal case was dropped, but it seems almost certain he was guilty. He was definitely guilty of the aftermath: he hired lawyers to destroy a young woman’s reputation.

His apology, lauded by some as exemplary, was additional proof that he couldn’t see others fully. He was blinded by himself, just as he blinded so many of us for too long.

It’s a beautiful essay on the inability to let go of Bryant the hero despite what she knows of Bryant the person. He moulded her. She moulded herself after him. But note the repeated references to his self obsession.

No one held his hand and opened his eyes to another, more accurate vision of himself. He never saw himself clearly—not on his first day, not on his last.

Kobe’s impaired vision is fundamental to what made him one of the greatest players in the history of the NBA. He thought he could do the impossible, and that belief made the impossible possible, again and again: playing through a dislocated finger , making both free throws after tearing his Achilles, forcing overtime with a buzzer beating 3 and then winning that game with a fadeaway three-pointer in double overtime, those eight-one points .

That belief is integral to success has been drilled into me. “Sooner or later the man who wins is the one who thinks he can” goes a line from one of my grandpas favourite poems. NBA commentators will often remark on how necessary it is for shooters. That they took the next shot as if they forgot the last one.

But Menon, Bryant, shows it can go too far. It can blind you to others.

This summer I read a great book about the process of basketball: the art of a beautiful game by Chris Ballard. The Kobe chapters are, somewhat predictably, about his legendary competitiveness. Note how early it starts.

He keeps bugging Brian Shaw, then a star player in Europe, to play him one-on-one. Eventually Shaw relents, and the two play H-O-R-S-E. “To this day, Kobe claims he beat me,” says Shaw. “I’m like, right, an 11-year-old kid, but he’s serious.”

Now Kobe is 13 years old and an eighth-grader in the suburbs of Philadelphia, skinny as a paper clip. He is scrimmaging against varsity players at Lower Merion High in an informal practice. They are taken aback. “Here’s this kid, and he has no fear of us at all,” says Doug Young, then a sophomore on the team. “He’s throwing elbows, setting hard screens.”

Bryant, now 17, is to play one-on-one against Michael Cooper, the former Lakers guard and one of the premier defenders in NBA history. Cooper is 40 years old but still in great shape, wiry and long and much stronger than the teenage Bryant. The game is not even close. “It was like Cooper was mesmerized by him,” says Ridder, now the Warriors’ director of media relations. After 10 minutes, West stands up. “That’s it, I’ve seen enough,” he says. “He’s better than anyone we’ve got on the team right now. Let’s go.”

The examples are endless. Bryant’s belief in his own powers started young and apparently drove him to greatness. It was plain every time he took the court. You could see it in his eyes.

But Ballard also reveals the flip side. Of Bryant basically tormenting teammates through an obsession with winning. How, as Menon noted, his self-obsession often came at others’ expense.

Now it’s 2000, and Bryant is an All-Star and a franchise player. Still, when guard Isaiah Rider is signed as a free agent by the Lakers, Bryant forces Rider to repeatedly play one-on-one after practice to house-break this newest potential alpha male. (Bryant wins, of course.) When Mitch Richmond arrives the next year, it’s the same. “He was the man, and he wanted us to know it,” says Richmond. “He was never mean or personal about it; it’s just how he was.”

Unfortunately, this is probably what I will take away from Bryant. He was a joy to watch compete. Not just gifted but amazingly driven to be the best. But there was also a nasty side to that. What drove him to greatness likely drove him too far.

If you have thought seriously about inequality or capitalism then the thesis of The code of capital by Katharina Pistor is not going to be too shocking. Still, it’s one of the best articulated explanations for wealth and inequality I’ve seen.

Fundamentally, capital is made from two ingredients: an asset, and the legal code. I use the term “asset” broadly to denote any object, claim, skill, or idea, regardless of its form. In their unadulterated appearance, these simple assets are just that: a piece of dirt, a building, a promise to receive payment at a future date, an idea for a new drug, or a string of digital code. With the right legal coding, any of these assets can be turned into capital and thereby increase its propensity to create wealth for its holder(s)…

This gives you the crux of the argument. That how assets are turned into capital, and so generate wealth and confer certain rights, is contrived. This may seem like a simplistic and obvious point, but we often take for granted what protections are afforded to certain assets, why some stakeholders are held over others, or the myriad frameworks and legal fictions we use to interact with them.

Who decides this, and why, helps explain persistent and widening inequality in many societies.

The idea that people aren’t all equal before the law isn’t a novel concept. Nor is the notion that the already wealthy and powerful have greater ability to influence this. But Pistor makes a more subtle point.

She focuses on how private agents, through private law, have taken over this process. Lawyers, not legislators, conform assets to the law, and select and fashion the law to suit.

Law is the cloth from which capital is cut; it gives holders of capital assets the right to exclusive use and to the future returns on their assets; it allows capital to rule not by force, but by law. The cloth is woven of private law, of contracts, property rights, trust, corporate, and bankruptcy law, the modules of the code of capital. Capital owes its vibrancy and frequent transmutations (from land, to firms, to debt, to ideas, etc.) to the fact that private and not state actors code capital in law.

In that sense the issue is less about access to legislators and more about access to smart and creative lawyers. Especially interesting is how Pistor frames this as an implicit subsidy to those able to play the game.

Subsidies and other “entitlements” are typically viewed with great suspicion, because they are regarded as distortive of markets and lead to inefficiencies, even corruption. Yet, the legal protections capital enjoys are arguably the mother of all subsidies. Without the code’s modules and the possibility to fashion them to one’s liking, neither capital nor capitalism would exist.

What Pistor reveals is a deeper problem than is acknowledged by calls for higher taxes, different tax treatment, greater transfers, or pretty much anything else intended to decrease inequality. This is a structural issue, an invisible force behind much of our lives. It’s about who gets to set the rules behind closed doors.

Realizing the centrality and power of law for coding capital has important implications for understanding the political economy of capitalism. It shifts attention from class identity and class struggle to the question of who has access to and control over the legal code and its masters: the landed elites; the long-distance traders and merchant banks; the shareholders of corporations that own production facilities or simply hold assets behind a corporate veil; the banks who grant loans, issue credit cards, and student loans; and the non-bank financial intermediaries that issue complex financial assets, including asset-backed securities and derivatives…

…The law is a powerful tool for social ordering and, if used wisely, has the potential to serve a broad range of social objectives; yet… the law has been placed firmly in the service of capital.

During his time helping to run the Medallion fund, Elwyn Berlekamp came to view the narratives that most investors latch on to to explain price moves as quaint, even dangerous, because they breed misplaced confidence that an investment can be adequately understood and its futures divined. If it was up to Berlekamp, stocks would have numbers attached to them, not names.

Being so certain and deterministic tricks us into believing the market is knowable. After all, these journalists have brought the word from on high. They have revealed the market’s Rube Goldberg nature. Can I not learn these secrets too?

Another lesson of the Renaissance experience is that there are more factors and variables influencing financial markets and individual investments than most realize or can deduce. Investors tend to focus on the most basic forces, but there are dozens of factors, perhaps whole dimensions of them, that are missed. Renaissance is aware of more of the forces that matter, along with the overlooked mathematical relationships that affect stock prices and other investments, than most anyone else.

That “more” in the last sentence is probably the key. It’s not that they understand the market. But they’re looking at more of it. Well past the simplistic correlations that can be delivered in thirty seconds.

One of the most galling things about politics and policy is how ancillary “facts” and logic can be. The internet has lowered the bar to information and expertise to almost nothing, but has devalued them as well.

The utopia of evidenced based policy seems unlikely to arrive. Even putting aside vested interests. This is sort of related to my previous post on how fond we are of reasoning through faulty analogy. But I wonder if it is not a more fundamental issue of the coldness of facts.

This thought struck while reading The Man Who Solved the Market by Gregory Zuckerman. The book traces the story of Jim Simons and Renaissance Technologies. They were some of the pioneers of quantitative trading and have had almost unparalleled success over decades.

What separates Renaissance (at least initially) is that Jim and most of the others were mathematicians and computer scientists. Many didn’t have any experience, or even interest in, finance. Hence their goal was to create an automated trading system:

Humans are prone to fear, greed, and outright panic, all of which tend to sow volatility in financial markets. Machines could make markets more stable, if they elbow out individuals governed by biases and emotions. And computer-driven decision-making in other fields, such as the airline industry, has generally led to fewer mistakes.

But throughout the story you can feel a tug, between thus clear drive towards a fully automated system and a reticence to cede control. This was especially clear in times of turmoil and as the models became so complex and self-learning that it was all but impossible to understand why exactly a trade was being made (or reccomended).

Then, something unexpected happened. The computerized system developed an unusual appetite for potatoes, shifting two-thirds of its cash into futures contracts on the New York Mercantile Exchange that represented millions of pounds of Maine potatoes. One day, Simons got a call from unhappy regulators at the Commodity Futures Trading Commission: Monemetrics was close to cornering the global market for these potatoes, they said, with some alarm. Simons had to stifle a giggle. Yes, the regulators were grilling him, but they had to realize Simons hadn’t meant to accumulate so many potatoes; he couldn’t even understand why his computer system was buying so many of them. Surely, the CFTC would understand that.

Soon, he and Baum had lost confidence in their system. They could see the Piggy Basket’s trades and were aware when it made and lost money, but Simons and Baum weren’t sure why the model was making its trading decisions. Maybe a computerized trading model wasn’t the way to go, after all, they decided.

What’s striking is that these were all people who understood the underlying logic of such a system. That while it’s (probably) impossible to predict any particular stock or commodity, there are patterns in the data. Patterns that represent biases, mistakes and other phenomena. That these can be identified. And, given enough “bets”, they only had to be correct 50.075% of the time to make an absolute killing.

Most of them had been involved in the construction of the model. Many had previously done academic research, and even invented techniques, on which it was based. And yet they still had trouble trusting something they didn’t fully understand.

Some rank-and-file senior scientists were upset—not so much by the losses, but because Simons had interfered with the trading system and reduced positions. Some took the decision as a personal affront, a sign of ideological weakness and a lack of conviction in their labor. “You’re dead wrong,” a senior researcher emailed Simons. “You believe in the system, or you don’t,” another scientist said, with some disgust.

Of course, they were also aware of the inverse – how fallible human traders are. And that many of their own mistakes, especially early on, were the result of human intervention.

But it was obviously hard and contentious to move beyond this hill. To trust something they didn’t fully understand. The “facts” from nowhere. The truth without a good story. Maybe this is the problem with evidence based policy.

A couple of years ago I went on a three day trek that all but shattered my love of hikes. It was a hilly circuit, slippery and hot. But on the third day, as we slowly ran out of lollies, food, patience and even water; we were repeatedly greeted by false summits.

The successes of deep learning have been truly remarkable and have caught many of us by surprise. Nevertheless, deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not. It has not addressed the truly difficult questions that continue to prevent us from achieving humanlike AI.

Artificial Intelligence has famously had a few “winters”, as what seemed like fundamental breakthroughs petered out. Similarly, the list of “transformational” technologies that failed to make a real dent is incredibly long.

For the non technical among us it can be very easy to mistake these kinds of false summits for fundamental transformation. Especially as they often do represent some progress. Finding themselves in products that we actually use or glimpse on breakfast television etc.

Part of the problem is framing. Especially as the incentive for so many is to hype. But it’s also a focus on outcomes rather than process.

The torture of that trek came from us focusing on the end rather than the journey. We lost track of the scenery, fresh air and each other.

We get tricked by technological false summits in the same way. By focusing so intently on what the technology can do. But the real power comes from looking at the process. Both the roadblocks and potential from questioning what the difficult questions are.